Joelle Guerra

☕Most AI training creates spectators. This one creates builders. I met the founders of The Gen Academy last week in San Francisco and their conviction is hard to argue with. In 2025, they kept seeing the same thing: ambitious professionals surrounded by AI tools… who didn't know how to actually build with them. Tutorials taught syntax. Conferences sold inspiration. Almost no one taught the messy middle... how to design, ship, and trust real AI systems. So they built the program they wished existed. For builders. Not spectators. What struck me most: -> Learn by building : every workshop ships a working project. Notes are nice, repos are better. -> Uses cases and the ecosystem as the classroom : learners work with the real building blocks from different tech partners. No toy demos in a notebook. -> A global community from day one : Cohort 1 sold out, with professionals from tech giants, financial institutions, consulting firms and retail leaders… across more than ten countries. And their take on go to market goes well beyond education: "You don't sell to developers. You earn them." What made this encounter particularly inspiring for me: rarely do you find coaching, mentoring, functional expertise and deep technical knowledge all in the same room. Most training programs offer one or the other. Here, it's all of it and that very smart combination hits differently ! It changes the questions you ask. And the ones you bring back home. In a world where AI tools shift every week, the real question is no longer "who has the best model" - it's who is actually training people to build with it. Thank you Aishwarya Srinivasan and Arvind Narayanamurthy for the conversation. I wish the best to your GenAcademy 😀 For more info : lnkd.in/eEcj_TfB *** Pour quelques jours, les Pauses IA passent à l'anglais :) même format, même heure ou presque, autre langue. Une immersion de terrain, pour ramener l'essentiel aux équipes et aux projets.*** #AI #AIEducation #GenAI #BuildersNotSpectators

May 18, 2026

Tanmay Pathak

I'll be honest — I went into today's session 'How AI Changes Design: A Practical Look' thinking I already 'got' how AI fits into design. I was wrong. 'How AI Changes Design: A Practical Look' with Aishwarya Srinivasan, Arvind Narayanamurthy and Aishwarya Rao reframed how I think about the AI products. The part that hit hardest? Aishwarya Rao's point that AI isn't just a faster way to do the same job — it changes what the job *is*. That's a different conversation than most people are having. Whether you're in design, product, or engineering — if AI touches your work in any way, the frameworks from this session are worth your time. Huge appreciation for industry experts who bring genuine depth to the conversation — this is the kind of session that actually shapes how you think and build. 💡 🙌

May 18, 2026

Mujahid Mirza

Driving Data Strategy & Insights | BI, AI & Data Engineering

Last weekend, I attended a session on 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗖𝗹𝗮𝘂𝗱𝗲 𝗖𝗼𝘄𝗼𝗿𝗸 hosted by Aishwarya Srinivasan and Arvind Narayanamurthy, and it shifted how I think about data exploration and problem-solving. 𝗞𝗲𝘆 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴𝘀: • 𝗜𝗻𝘀𝘁𝗮𝗻𝘁 𝗡𝗟 𝗘𝘅𝗽𝗹𝗼𝗿𝗮𝘁𝗶𝗼𝗻 - Natural language forces clearer problem definition before diving into implementation (No Sql) • 𝗥𝗮𝗽𝗶𝗱 & 𝗟𝗜𝗩𝗘 𝗣𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 - Accelerated hypothesis testing that I'm now applying to my BI workflows • 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 - Breaking down schemas through conversation reveals insights I'd miss traditionally • 𝗥𝗲𝘂𝘀𝗮𝗯𝗹𝗲 𝗗𝗲𝘀𝗶𝗴𝗻 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 - The markdown-based approach to documentation is game-changing To validate these learnings, I built a FIFA 2022 analytics dashboard using Claude Cowork as a sandbox to test concepts before applying them to enterprise dashboards. 📊 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱: lnkd.in/g6m9PVzQ 🔗 𝗚𝗶𝘁𝗛𝘂𝗯: lnkd.in/gteareCy Are you experimenting with these shifts? Would love your thoughts.

May 5, 2026

Anand Sridharan

Service Delivery Transformation | Leading Intelligent Ops | Data & AI

From Text to SQL: How SQL AI Agents are Transforming Data Access Wrapped up an incredible session on 𝗦𝗤𝗟 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 with Rami Krispin, where he shared his rich experience and production ready play-book. Encapsulated - Top 5 pointers (provided AI generated image for illustration only) //Context is King, Not the Model You don't need the fanciest LLM to build powerful SQL agents. A thoughtfully engineered prompt with rich context (schema details, categorical values, business logic) can outperform expensive models. It's about 𝘩𝘰𝘸 you ask, not just 𝘸𝘩𝘰 you ask. // The Agent Loop is Everything It's not just LLM → Output. Real agents iterate through reasoning → tool calls → observation → refinement. This dynamic execution path is what separates true agents from simple API wrappers. // Safety Guardrails Aren't Optional : They're Essential Read-only access, query validation, row limits, logs and error handling aren't afterthoughts. They're foundational. One misconfigured query can bring down your entire database. Period. // Memory Management Changes the Game Adding conversation history transforms single-shot queries into meaningful dialogue. Users can ask follow-ups naturally. But manage it wisely, unbounded memory explodes your token costs. // Evaluation & Fallback Strategies = Smart Cost Optimization Test your models systematically. Sometimes GPT-4 mini outperforms GPT-4 Turbo at 1/4 the cost. Build intelligent fallback mechanisms; simple models for routine queries, powerful ones for edge cases. A heartfelt thank you to Aishwarya Srinivasan, Arvind Narayanamurthy for setting up context/de-briefing about AI agent, and bringing in Rami Krispin (SME) for this Lightening session. Your generosity in sharing examples/use cases, live demos are highly appreciated. I gained additional insights and I'm sure all the participants who attended "live" would have benefitted, and reach out to discord community for Q&A/follow-up. #SQLAIAgents #AIEngineering #LLMs #DataAccess #ProductionAI #TechEducation #LinkedInLearning

May 5, 2026

Yash Jaiswal

Power BI Developer and BI Specialist | 5 Years Enterprise BI | DAX | SQL | AI | Microsoft Fabric | Open to Senior Roles

Joined a free session today on Building SQL AI Agents and one thing was clear from the start. Getting an LLM to write SQL is easy. Getting it production ready is a completely different game. What actually separates a demo from a real system: autonomous context generation, SQL validation, safety checks , error handling and a debugger agent running in the background. The prompt isn't something you write once either. In production it gets assembled dynamically from your database schema, domain skills, and user question every single time. Shoutout to Aishwarya Srinivasan, Arvind Narayanamurthy, and Rami Krispin for keeping it brutally honest and technically sharp.

May 5, 2026

Haadvitha Nimmagadda

Analytics Engineer | SQL | Power BI | Azure | Databricks

Attended a session on Building SQL AI Agents at The Gen Academy today. Honest take? Parts of it felt overwhelming. As someone just starting to explore AI, I could follow the high-level ideas, but the deeper discussions around context, architecture, and guardrails made one thing clear: There’s a lot to learn here. And that was the most useful part. Before writing a single line of code, the session broke down how agents actually work: • Agents don’t respond in one step, they run a loop: Think → Act → Observe until the goal is met • There are key levers you can control to shape that loop • Every agent is built from a set of core building blocks • Not every problem needs an agent, knowing when to use one matters Understanding these fundamentals before building matters more than I expected. Then it got more practical, what it takes to move from idea to something usable: • The gap between a polished demo and a production system is significant • Context is more than a prompt, it includes instructions, knowledge, and state • Guardrails and validation are what make these systems reliable Instead of trying to learn everything upfront, I’ve decided to start building a simple SQL agent and learn through the process. Thanks to Aishwarya Srinivasan, Arvind Narayanamurthy and Rami Krispin for keeping it practical and grounded. #AIAgents #GenAI #SQLAgent #DataEngineering #DataAnalytics

May 5, 2026

Vaishnavi Patil

Data Science & Analytics @ Exelon (BGE) | SQL • Python • ML • Power BI/Fabric • Tableau | Forecasting, KPI Insights, Databricks, Azure | Turning Data into Scalable Business Impact

🚀 Spent my weekend diving into Building SQL AI Agents — and it was genuinely one of the most practical AI sessions I’ve attended. Really enjoyed learning how LLMs + agents are moving from demos to real production systems — especially: Natural language → SQL in real-world use cases Context engineering for reliable outputs Agent workflows (tools, planning, memory) Importance of guardrails & data access control Loved how everything tied back to actual data + enterprise systems, not just theory. Thank you Aishwarya Srinivasan Rami Krispin and Arvind Narayanamurthy! Looking forward to diving deeper into this space and building something hands-on soon. #AI #LLM #SQL #AIAgents #DataEngineering #RAG #TechLearning

May 5, 2026

Yuvansh Bhardwaj

AI Engineer | Data Scientist | Gen AI Specialist

Attended an incredible session on 𝗕𝘂𝗶𝗹𝗱𝗶𝗻𝗴 𝗦𝗤𝗟 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 hosted by Aishwarya Srinivasan, Arvind Narayanamurthy, and Rami Krispin. 𝗜𝗳 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗯𝗿𝗮𝗶𝗻 𝗼𝗳 𝘁𝗵𝗲 𝗔𝗜 𝗲𝗰𝗼𝘀𝘆𝘀𝘁𝗲𝗺, 𝘁𝗵𝗲𝗻 𝗱𝗮𝘁𝗮 𝗮𝗻𝗱 𝗮𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗮𝗿𝗲 𝘁𝗵𝗲 𝗳𝘂𝗲𝗹. So I went ahead and built my own SQL AI Agent from scratch. What I built: ✅ Natural language to SQL using Ollama (free, no API key) ✅ SQLite database with zero setup ✅ Streamlit UI with query history and CSV export ✅ Runs 100% locally, no cloud, no cost Huge thanks to Aishwarya, Arvind, and Rami for making this so hands-on. Sessions like this push you to stop watching and start building. Check out the repo here: 🔗 lnkd.in/ebX4Dqix #AI #SQLAgent #Ollama #LLM #DataScience #ArtificialIntelligence #DataEngineering #MachineLearning #Python #Canada

May 5, 2026

Karthic Kumar Eswari Subramanian

Wow, just finished the "Vibe Coding: Code Smarter with AI" course by Aishwarya and Arvind, and I'm absolutely buzzing! This wasn't just another coding tutorial; it was a truly vibrant and insightful experience. The way Aishwarya and Arvind broke down how to leverage AI tools like Replit AI and Cursor AI to build practical applications was fantastic. Seeing a sample portfolio app come to life with Replit AI and then diving into a financial portfolio web app using Cursor AI really solidified the concepts. It wasn't just theoretical; it was hands-on and immediately applicable. What truly set this course apart was the engaging and patient way Aishwarya and Arvind addressed everyone's questions. No matter how big or small, they took the time to provide clear and helpful answers. Even when a few of us were clearly in "vibe nap" mode between coding sessions (we've all been there!), Aishwarya and Arvind had a knack for bringing us back into the flow and ensuring we didn't miss crucial points. It speaks volumes about their teaching style that even in those moments, the explanations were clear enough to still grasp the core ideas and improve our skills. The course content was incredibly well-structured, and the pace felt just right. You could tell that Aishwarya and Arvind are not only experts in their field but also passionate about sharing their knowledge. Their enthusiasm was infectious! And you know what? Forget complex algorithms and years of cryptic syntax! It turns out, channeling our inner Captain America, the secret weapon to building amazing applications with AI assistance boils down to one simple truth: "Whatever It Takes!" ...which, in this context, hilariously seems to mean just knowing plain English to tell the AI what you want. Who knew building the future could be this...linguistically straightforward? Thanks, Aishwarya and Arvind, for showing us the accessible path to code smarter! Aishwarya Srinivasan Arvind Narayanamurthy Deploying code failed and here is the pdf format of the vibe course portfolio that I built with the AI tool :)

May 4, 2026

Balavinayagam Shanmugavel

Architecture & Engineering | Core Banking · Lending · Private & Consumer Banking · Loyalty | Microservices · AWS · Event-Driven Architecture | Generative & Agentic AI

RAG Workshop Takeaways – Powered by Aishwarya Srinivasan & Arvind Narayanamurthy Recently, I had the opportunity to attend an insightful online RAG workshop conducted by Aishwarya Srinivasan and Arvind Narayanamurthy on July 26 (11 PM) to July 27 (2 AM) SGT. Despite the late hours, it was absolutely worth staying up for — the session was packed with conceptual, practical knowledge and real-world techniques! Here are my key takeaways: 🔹 RAG (Retrieval-Augmented Generation) Fundamentals → Deep dive into how RAG augments LLMs by injecting fresh, contextual data at runtime, overcoming static training limitations. 🔹 Live Demo → A complete end-to-end RAG pipeline demo using LangChain and Pinecone – from ingestion to generation. 🔹 Hands-On: Building a Basic RAG Workflow → Pipeline Steps: Data Extraction – From a live website Chunking – Breaking down into manageable segments Indexing – Using ChromaDB and Google's Generative AI Embeddings Retrieval – Based on vector similarity Generation – Powered by Gemini 1.5 Flash 🔹 Metadata-Aware RAG → Enhancing retrieval precision by extracting and indexing metadata alongside content for filtered, targeted queries. 🔹 Hybrid RAG Architecture → Combining semantic search (vector-based) with lexical/keyword search for balanced relevance and coverage. 🔹 Similarity Techniques → Leveraging cosine similarity to improve document-query matching effectiveness. 🔹 Architecture Patterns & Debugging Tips → Field-tested practices and design examples for building production-grade RAG systems. Thank you Aishwarya Srinivasan and Arvind Narayanamurthy for this power packed session, looking forward for more hands-on sessions 👍 #RAG #AI #LLM

Apr 26, 2026

Sunny Rodrigues

✍🏻 student forever // onboarding + project ops + customer success + product @ scale (saas, ai, social) // google + meta alum // vibe-coder // built on making a difference & curiosity in 🌁

I spent my Saturday at the Context Engineering Workshop with The Gen Academy, and here’s the thing. Some sessions simply teach you something. This one actually shifted how I think. It was all about Prompt Engineering until I heard about Context Engineering. So what is it ?? Context engineering sits at the heart of building reliable AI systems, but very few people break it down in a way that’s practical, structured, and immediately usable. This workshop did exactly that. Huge thank you to Aishwarya Srinivasan, Arvind Narayanamurthy, and the entire Gen Academy team for creating a space where learning feels both rigorous and accessible. I walked away with frameworks I can apply right away, along with a sharper mental model for how to design context in a way that elevates performance, clarity, and trust. For anyone exploring agentic workflows, retrieval, or prompt engineering at a deeper level, the teaching and the materials are an amazing source for both technical and non-technical folks. Grateful to have been part of this one and excited to keep sharpening this skillset. #ai #contextengineering #machinelearning #agents #llms #genai #community #learning #sanfrancisco #sftech

Apr 26, 2026

LI

Lavanya Nallamshetty, PMP, ITIL

Attended an incredible RAG Workshop over the weekend! The trainers, Aishwarya Srinivasan and Arvind Narayanamurthy did a fantastic job explaining the concepts clearly and breaking down complex ideas in such an accessible way. The workshop was thoughtfully structured, with a perfect balance of theory and hands-on practice, which made the learning experience so much more effective. If you’re interested in Retrieval-Augmented Generation (RAG) and building real world AI applications, this is something to checkout. Part 2 and Part 3 are coming up soon I believe.

Apr 26, 2026

Geetha priya J

AI Engineer @American Express

I attended a hands-on workshop over the weekend on LangChain and LangGraph led by Aishwarya Srinivasan and Arvind Narayanamurthy, and it was an incredibly valuable learning experience. The session was well structured, starting with foundational concepts and gradually moving into practical, real-world implementations of LangChain and LangGraph, clearly explaining how and when to use each. LangGraph is a framework for building stateful, controllable, graph-based AI workflows. • It lets you design LLM applications as graphs with nodes, edges, and shared state, enabling: • Memory across steps for long, multi-step workflows • Precise control over how an agent thinks and acts • Safe, structured tool calls inside workflows LangChain, on the other hand, focuses on building production-ready LLM applications by providing: • Components for prompts, models, chains, tools • Simplified orchestration of LLM logic and memory • Standardized patterns for real-world use cases A practical, hands-on session that seamlessly connected concepts with implementation. Huge thanks to Aishwarya and Arvind for delivering such an engaging and well-executed workshop. #LangChain #LangGraph #LLM #AIEngineering #AgenticAI #Learning #AI #workshop

Apr 26, 2026

Laura Ruiz

Associate Director, Data Science @AstraZeneca

Just a few days ago, I spent nearly 5 hours in an incredible hands-on workshop on 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗪𝗼𝗿𝗸𝗳𝗹𝗼𝘄𝘀 𝗮𝗻𝗱 𝗠𝗼𝗱𝗲𝗹 𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗣𝗿𝗼𝘁𝗼𝗰𝗼𝗹 (𝗠𝗖𝗣) implementation! Led by the brilliant Aishwarya Srinivasan and Arvind Narayanamurthy, this session was honestly one of the clearest explanations of this topic I've ever seen so far. We dove deep into: - 𝗠𝗖𝗣: What Model Context Protocol (MCP) is, distinguishing its role in standardizing AI agent interactions from traditional function calling. We also covered the nuances of its transport layers: stdio (for co-located client-server) vs. sse (for pre-hosted server connections). - 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 𝘃𝘀. 𝗟𝗮𝗻𝗴𝗖𝗵𝗮𝗶𝗻: Explored their distinct advantages, particularly how LangGraph facilitates building more stateful, non-linear workflows using Agentic patterns like ReAct (Reason + Act) and Reflection, which is crucial for complex, adaptive agents. - 𝗥𝗔𝗚: Explanation and common misconceptions around Retrieval-Augmented Generation (RAG), emphasizing that it's distinct from large context windows and function calling. We saw how RAG ensures factual integrity and contextual grounding by integrating external knowledge stores. - 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗣𝗮𝘁𝘁𝗲𝗿𝗻𝘀 & 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻: A deep dive into patterns like ReAct and Reflection, empowering agents to autonomously plan and execute multi-step actions. - 𝗣𝗿𝗮𝗰𝘁𝗶𝗰𝗮𝗹 𝗜𝗺𝗽𝗹𝗲𝗺𝗲𝗻𝘁𝗮𝘁𝗶𝗼𝗻: Live coding sessions, including building an MCP server from scratch and a sample product support agent using Gemini models. - 𝗦𝗲𝗰𝘂𝗿𝗶𝘁𝘆 & 𝗢𝗯𝘀𝗲𝗿𝘃𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Discussed critical aspects of agent safety, from preventing prompt injection to implementing human-in-the-loop approvals, and touched upon tools for LLM observability in production environments. If you have a technical background and want to deep dive on these concepts, I recommend checking out the workshop recording. It's available for a very reasonable price here: lnkd.in/dAU7VvCU Huge thanks again to Aishwarya Srinivasan and Arvind Narayanamurthy for such an impactful session! #AgenticAI #LangGraph #Langsmith #ZapierAI #MCP #RAG #ReActPattern #ReflectionPattern

Apr 26, 2026

Lakshmi Priya Gayatri Vutukuri

Data Scientist @ World Bank | Data Science | AI | ML | NLP | Azure

✨ I attended an incredible hands-on workshop over the weekend on Agentic AI Workflows and Model Context Protocol (MCP) implementation led by Aishwarya Srinivasan and Arvind Narayanamurthy! It was such a valuable and enriching learning experience. This session was a deep dive into MCP and Agentic AI systems. We started from the basics and worked our way up to building an MCP server live, creating AI agents from scratch. 🤖⚙️ I took away so much from this session, but here are some of the key things I have learned: ✅ What MCP is and how it is different from function calling, enabling AI agent interactions. ✅ The transport layers in MCP - understanding when to use stdio (when the client spawns the server making them both in the same location) vs. sse (when the MCP server is already hosted and the client connects to it). 🔌🌐 ✅ Differences and use cases of LangGraph vs. LangChain, and how LangGraph allows building more stateful workflows using Agentic patterns like ReAct (Reason + Act) and Reflection. 🔄🧠 ✅ Understanding the trade-off between autonomy and control in AI systems and how to design for the right balance depending on the use case. ⚖️🤔 ✅ Building an MCP server from scratch 🛠️ ✅ Practical implementation of a real-world agent use case using LangGraph and ReAct Pattern 🌍💡 What stood out to me personally was how the session cleared up common misconceptions around RAG, function-calling, context windows, and how MCP fits into the bigger picture. The live coding part was a game-changer 💻🚀 — seeing things in action made the concepts click and has given me much more confidence in using these tools in the future. Huge thanks to Aishwarya and Arvind, for driving this fantastic initiative and delivering such an engaging and practical session 🙌👏. I truly enjoyed the energy, the depth of content, and the clarity you both brought to complex topics. It’s always exciting to keep learning in this fast-evolving field 🌟📚. #AgenticAI #LangGraph #LangChain #MCP #AIagents #ReActPattern #ReflectionPattern #AIworkflows #RAG #AutonomousAgents #AgenticPatterns

Apr 26, 2026

Rushabh Dev Kashyap

VP, Data Strategy for AI & Governance @ JPMC | ex-IBMer | Data platforms, SaaS/PaaS products, Big Data

🚀 Just attended a mind-blowing workshop on Agentic AI Workflows and Model Context Protocol (MCP) implementation with the incredible Aishwarya Srinivasan and Arvind Narayanamurthy. Truly one of the most insightful and energising sessions I’ve been part of! It was a deep dive into the evolving world of #LLMs, #RAG, and #AgenticAI. I asked Perplexity to summarise some notes I took down during the workshop for generic consumption and here's what stood out: 🔹 LangChain vs LangGraph LangChain offers a powerful abstraction layer to build AI apps quickly. LangGraph enables stateful workflows and is ideal for unstructured data and nested/non-linear logic — perfect for building complex agents! 🔹 RAG (Retrieval-Augmented Generation) decoded: Components: Query processing, Embedding, Vector Store, Context Integration, Response ❗Why RAG? To ensure factual integrity, contextual grounding, and for complete ownership on knowledge store. Misconception: A large context window ≠ RAG. Nor is function calling the same as RAG! 🔹 MCP (Model Context Protocol) in action: Standardizes communication between LLMs and external tools via a server-client architecture. ❗An MCP server can be connected to a single tool or package multiple tools together (e.g., Gmail, Slack, Google Drive). The client makes requests and knows which server/tool to use. ❗Transport layers: stdio (client-spawned, co-located) and sse (pre-hosted, accessible remotely). ✅ Crucial: Sanitize outputs, use trusted servers, enforce OAuth/API key-based access. 🔹 Agentic Patterns & Execution We explored the ReAct (Reason + Act) pattern and the Reflection pattern. These patterns empower agents to autonomously plan and take multi-step actions. LangGraph makes these patterns stateful, solving a major limitation of stateless LLMs. 🔹 Using Prebuilt Agents for building typical ReAct patterns - from langgraph.prebuilt import create_react_agent from langchain_google_genai import ChatGoogleGenerativeAI Built a sample Product support agent to with reasoning support using Gemini models. (gemini-2.5-flash-preview-04-17) 🔹 Security & Observability Agent safety isn’t just a buzzword: From preventing prompt injection to human-in-the-loop approvals — it's foundational. 🧠 Wondering: What are some other tools like LangSmith that help with LLM observability in production environments? 🛠️ Langchain-MCP adapters enable tool compatibility across ecosystems. 📊 Fine-tuning, prompt design, function calling — each has a role in aligning model behavior to business needs. Last but not the least, thoroughly enjoyed the engaging conversations with fellow enthusiasts and learners from across the world! Cheers! #AI #MachineLearning #LLM #LangChain #LangGraph #MCP #AgenticAI #RAG #Gemini #PromptEngineering #FunctionCalling #AutonomousAgents #AIWorkflows #EnterpriseAI #VectorSearch #OpenAI #Anthropic #ZapierAI #Langsmith #Observability #SecureAI #DataAndAI

🚀 Just attended a mind-blowing workshop on Agentic AI Workflows and Model Context Protocol (MCP) implementation with the incredible Aishwarya Srinivasan and Arvind Narayanamurthy. Truly one of the most insightful and energising sessions I’ve been part of! It was a deep dive into the evolving world of #LLMs, #RAG, and #AgenticAI. I asked Perplexity to summarise some notes I took down during the workshop for generic consumption and here's what stood out: 🔹 LangChain vs LangGraph LangChain offers a powerful abstraction layer to build AI apps quickly. LangGraph enables stateful workflows and is ideal for unstructured data and nested/non-linear logic — perfect for building complex agents! 🔹 RAG (Retrieval-Augmented Generation) decoded: Components: Query processing, Embedding, Vector Store, Context Integration, Response ❗Why RAG? To ensure factual integrity, contextual grounding, and for complete ownership on knowledge store. Misconception: A large context window ≠ RAG. Nor is function calling the same as RAG! 🔹 MCP (Model Context Protocol) in action: Standardizes communication between LLMs and external tools via a server-client architecture. ❗An MCP server can be connected to a single tool or package multiple tools together (e.g., Gmail, Slack, Google Drive). The client makes requests and knows which server/tool to use. ❗Transport layers: stdio (client-spawned, co-located) and sse (pre-hosted, accessible remotely). ✅ Crucial: Sanitize outputs, use trusted servers, enforce OAuth/API key-based access. 🔹 Agentic Patterns & Execution We explored the ReAct (Reason + Act) pattern and the Reflection pattern. These patterns empower agents to autonomously plan and take multi-step actions. LangGraph makes these patterns stateful, solving a major limitation of stateless LLMs. 🔹 Using Prebuilt Agents for building typical ReAct patterns - from langgraph.prebuilt import create_react_agent from langchain_google_genai import ChatGoogleGenerativeAI Built a sample Product support agent to with reasoning support using Gemini models. (gemini-2.5-flash-preview-04-17) 🔹 Security & Observability Agent safety isn’t just a buzzword: From preventing prompt injection to human-in-the-loop approvals — it's foundational. 🧠 Wondering: What are some other tools like LangSmith that help with LLM observability in production environments? 🛠️ Langchain-MCP adapters enable tool compatibility across ecosystems. 📊 Fine-tuning, prompt design, function calling — each has a role in aligning model behavior to business needs. Last but not the least, thoroughly enjoyed the engaging conversations with fellow enthusiasts and learners from across the world! Cheers! #AI #MachineLearning #LLM #LangChain #LangGraph #MCP #AgenticAI #RAG #Gemini #PromptEngineering #FunctionCalling #AutonomousAgents #AIWorkflows #EnterpriseAI #VectorSearch #OpenAI #Anthropic #ZapierAI #Langsmith #Observability #SecureAI #DataAndAI

Apr 26, 2026

SP

Sunil Pentapati

Technology Executive | Strategy | Data and AI Platform Engineering | Thought Leadership

Midnight coding hits differently, and it’s been a while. This Saturday, I felt that vibe again. :) I attended a Vibe coding workshop hosted by Aishwarya and Arvind in partnership with Replit. Using Replit and Cursor, I built a UI and spun up a Streamlit project. From my experience, scaling anything successfully requires: - A framework to streamline - Patterns and best practices to drive reusability, efficiency, and adoption - Techniques to operationalize through automation and orchestration, enabling measurable and observable outcomes I came out with a few takeaways in each of these areas. #vibecoding #learning #streamlit

Apr 26, 2026

Pawan Simha

Student @ SNPSU | Gemini Certified | Aspiring AI Product Manager | Python • Machine Learning • Web Development | CR & HRD Coordinator

AI isn’t the problem anymore. Context is. Yesterday night I attended the 1st Lightning Lesson on Retrieval-Augmented Generation (RAG) by Aishwarya Srinivasan and Arvind Narayanamurthy (Maven × The Gen Academy ). And it completely changed how I think about building AI systems. Instead of relying on static training data, RAG enables systems to retrieve relevant information in real time, producing responses grounded in actual, verifiable data. What stood out wasn’t just the concept, but the system design behind it: 1. How embeddings capture semantic meaning. 2. How chunking improves retrieval quality. 3. How vector databases power fast similarity search. It made one thing clear: The future of AI isn’t just about bigger models. It’s about building systems that combine retrieval, context, and reasoning effectively. Lately, I’ve been diving deeper into how these architectures work under the hood, and the more I explore, the more compelling this space becomes. I’m currently exploring this further and planning to build something around it soon 🚀 Question for my Network: For those working with LLMs, what vector databases are you using in production? #GenerativeAI #RAG #VectorDatabases #AIProductManagement #BuildInPublic #MCA #TechInnovation #SNPSU #BeyondAcademics

Apr 26, 2026

Shivangi J.

GenAI Architect & Data Strategist | Building AI that thinks, acts, and serves people | Agentic AI • RAG • AI Safety ✨ | Y4D 🇮🇳 🇺🇸

🙏 Huge thanks to Aishwarya Srinivasan and Arvind Narayanamurthy for an outstanding workshop on RAG (Retrieval Augmented Generation). The depth of insights you both shared was incredible — moving beyond the basics into real design patterns and enterprise-grade architectures: 🔍 Simple & Branched RAG for foundational retrieval 🤔 HyDE & Corrective RAG for reasoning and self-checks 🧠 Agentic RAG with master/sub-agent orchestration & RBAC 🎨 Multimodal RAG handling text, images, audio, video at scale 🕸 Graph RAG enabling multi-hop reasoning & auditability through knowledge graphs I especially valued the deep dives into: 🧩 Memory hierarchies (short-term, session, long-term) ⚖️ Compliance & traceability (audit trails, citations, observability tools) 💰 Cost trade-offs across multimodal patterns 🛠 Tools in action — Neo4j, LangChain, ChromaDB, OpenAI, OpenCLIP ✨ Key Takeaways: • RAG is not just about chatbots — it’s about building scalable, explainable, and compliant systems. • Design patterns matter → Simple, Corrective, Agentic, Multimodal, and Graph RAG each unlock different capabilities. • Memory hierarchies (short-term, session, long-term) are essential to balance cost, speed, and persistence. • Compliance & auditability (via LangFuse, cloud-native tracing, or Graph provenance) must be built-in, not bolted on. • Cost trade-offs are huge in multimodal RAG → the right design pattern can mean 10x efficiency. 💡 Questions I’m still reflecting on: • How do we best balance deterministic workflows with LLM-driven non-determinism in enterprise systems? • In large-scale multimodal setups, how can we keep context fresh and relevant without overspending on tokens? • Will Graph RAG (reasoning + provenance) or Agentic RAG (orchestration + sub-agents) dominate enterprise adoption next? Thank you both once again for expanding our thinking — this workshop was a true masterclass. 🙌 #RAG #GenAI #GraphRAG #MultimodalAI #AgenticAI #LLMs #AIEngineering

Apr 26, 2026

Mohd Anas

Applied AI Engineer | Building RAG & Agentic AI Systems | LangChain • LLMs • GenAI | Open to Opportunities

Two weeks ago, I came across a post by Aishwarya Srinivasan One idea from that post stayed with me: don’t just learn AI — build something real. It made me reflect on how I had been learning. Like many students, I was stuck in a loop of watching tutorials, taking courses, and saving posts, but not actually building anything meaningful. So I decided to change that. Instead of consuming more content, I picked a problem and started building from scratch. No perfect roadmap, no detailed plan — just learning by doing and figuring things out along the way. That’s how I ended up building a Finance Agent powered by RAG (Retrieval-Augmented Generation): GitHub: lnkd.in/gz8_qJJP Live Demo: lnkd.in/gcQdPn7t The goal was to move beyond a simple chatbot and build something closer to a real AI system — one that connects to actual financial data, retrieves relevant information, and generates grounded, contextual responses. While building this, I also explored something I hadn’t focused on before — observability in AI systems. Using LangSmith, I started tracking how the system actually behaves: response accuracy, latency, and failure cases. Instead of just checking if the output “looks right,” I could now measure how well the system was performing. I even ran multiple experiments comparing factual accuracy and latency, which helped me identify where the system was failing and improve it iteratively. This completely changed how I approached building. Instead of guessing, I could now iterate based on real data — making the system not just functional, but more reliable. This experience made one thing very clear to me. Getting an AI model to work at a basic level is not that hard anymore. But building something that is reliable, useful, and closer to real-world applications — that’s where the real challenge (and learning) begins. The industry is clearly moving in this direction. It’s no longer just about how powerful your model is, but about how well your system performs in real scenarios — whether it can deliver value, scale, and be trusted. For me, this is just the beginning. If you're learning AI/ML right now, my biggest takeaway would be this: don’t wait to feel ready. Start building. You’ll learn much faster by doing than by just consuming content. Would love to hear your thoughts or feedback on the project — and curious to know, what are you building these days? #AI #GenAI #RAG #AgenticAI #MachineLearning #BuildInPublic #LearningInPublic

Two weeks ago, I came across a post by Aishwarya Srinivasan One idea from that post stayed with me: don’t just learn AI — build something real. It made me reflect on how I had been learning. Like many students, I was stuck in a loop of watching tutorials, taking courses, and saving posts, but not actually building anything meaningful. So I decided to change that. Instead of consuming more content, I picked a problem and started building from scratch. No perfect roadmap, no detailed plan — just learning by doing and figuring things out along the way. That’s how I ended up building a Finance Agent powered by RAG (Retrieval-Augmented Generation): GitHub: lnkd.in/gz8_qJJP Live Demo: lnkd.in/gcQdPn7t The goal was to move beyond a simple chatbot and build something closer to a real AI system — one that connects to actual financial data, retrieves relevant information, and generates grounded, contextual responses. While building this, I also explored something I hadn’t focused on before — observability in AI systems. Using LangSmith, I started tracking how the system actually behaves: response accuracy, latency, and failure cases. Instead of just checking if the output “looks right,” I could now measure how well the system was performing. I even ran multiple experiments comparing factual accuracy and latency, which helped me identify where the system was failing and improve it iteratively. This completely changed how I approached building. Instead of guessing, I could now iterate based on real data — making the system not just functional, but more reliable. This experience made one thing very clear to me. Getting an AI model to work at a basic level is not that hard anymore. But building something that is reliable, useful, and closer to real-world applications — that’s where the real challenge (and learning) begins. The industry is clearly moving in this direction. It’s no longer just about how powerful your model is, but about how well your system performs in real scenarios — whether it can deliver value, scale, and be trusted. For me, this is just the beginning. If you're learning AI/ML right now, my biggest takeaway would be this: don’t wait to feel ready. Start building. You’ll learn much faster by doing than by just consuming content. Would love to hear your thoughts or feedback on the project — and curious to know, what are you building these days? #AI #GenAI #RAG #AgenticAI #MachineLearning #BuildInPublic #LearningInPublic

Apr 26, 2026

Sai Harshitha Sivalingala

Software Engineer | Full-Stack & AI Automation | Built Open-Source tools used by 4.5K+ Devs | Python • Java • Node.js • React • AWS • JavaScript• Kafka

RAG works… until you realize you have no idea what it's doing. 🧠⚠️ You can build a RAG pipeline, plug in embeddings, connect an LLM, and it works, but you have no idea what's actually happening inside. 😶 Why is a query slow? What exactly got retrieved? Why did the model give a bad answer? Where do you even start improving? Everything just feels like a black box. While exploring this, I came across Verba (7.6k ⭐ ), a really solid open-source RAG built on Weaviate. It already had real users, which made it the perfect system to build observability on top of. Github Link : lnkd.in/g7eNSVni I set up a reverse proxy in front of Verba and intercepted its WebSocket stream. That lets me track latency, capture retrieved chunks, cache responses, and log everything, all without modifying the actual RAG application. And this is where things got interesting. It's not just about measuring latency or errors. The metrics that actually matter are different: p90, p95 latencies (what users actually feel), slow queries (the ones that break trust), cache hit rate (cost + speed), and most importantly, thumbs down feedback. Because a system can be fast, have zero errors, and still give a completely wrong answer. Only the user tells you that. This made me realize, RAG isn't just about building pipelines. It’s about making them observable, debuggable, and improvable. There's still one blind spot though the pre-retrieval phase (embeddings + search decisions). That’s what I'm planning to dig into next. Also, small reality check, I did use Claude to help initially… and it completely broke halfway 😅.Ended up debugging a lot of weird edge cases myself, which turned out to be the most valuable part. Thanks to Aishwarya Srinivasan for planting the seed for this idea.🙌 🔗 GitHub: lnkd.in/gq8iscZ2 If you are working with RAG, you will probably hit this wall sooner or later.💭 #RAG #LLM #AIEngineering #AIObservability #OpenSource #SystemDesign #BuildInPublic #AIProjects

Apr 26, 2026

Yugank Das

Building with ML, AI and Web Design

I was on my usual evening YouTube scavenging session today when I accidentally stumbled across a video by Aishwarya Srinivasan explaining the fundamentals on RAG. I had no idea what RAG even was before watching it. It always seemed like a far fetched concept that would never make sense to someone so young in the industry as me. Nevertheless, I began scouring the web to get a hold of the basics, and unexpectedly, the concept clicked. I recently got back into spamming Blitz games at Chess.com, so I figured building a chess chatbot was the perfect way to learn by doing. RAG stands for Retrieval-Augmented Generation. Instead of just asking an AI a question and hoping it knows the answer, you first retrieve relevant passages from your own documents and then ask the model to answer using those. As a result, you usually receive more grounded and foundationally correct answers from the model. I used three chess PDFs covering openings, middlegames, and endgames as the knowledge base for this, and built the full pipeline from scratch. As usual, Groq came to my rescue as the LLM for the project with the Llama-3.3-70B-Versatile model. ChromaDB was the perfect choice for vector databases for first-timers like me. And well, it turns out chunk size actually matters more than I thought it did. My chunks were too small and kept splitting context mid-thought, so I doubled the size to 1000 characters with 200-character overlap and retrieval got a lot cleaner. Not all embedding models are the same. Swapping from all-MiniLM-L6-v2 to all-mpnet-base-v2 gave me better semantic search and suddenly the chatbot could find stuff it was completely missing before. Querying only n=3 chunks was too stingy. Bumping it to n=10 gave the LLM way more context to work with. With 3 retrieved chunks, it acted as if I asked it about pickleball and not chess. Printing chunk sources and metadata during debugging was a game changer. The model's final answer alone tells you nothing about what actually went wrong in the retrieval step. Doing this sped up my debug game by a mile. It is a naive RAG system by design. I have not implemented any reranking, memory or agents for the sake of simplicity so that I could fully focus on understanding what was happening under the hood. This is pretty much the same reason I repudiated the suggestion of using Streamlit to build a UI and stuck to a CLI (Command Line Interface), which is easier to debug around. Honestly one of the most satisfying 5-hour builds I have done. If you are curious about RAG and do not know where to start, go watch Aishwarya's video. #RAG #LLM #GenerativeAI #MachineLearning #LearningInPublic #Chess #AIProjects #BuildInPublic #NLP #VectorDatabase

I was on my usual evening YouTube scavenging session today when I accidentally stumbled across a video by Aishwarya Srinivasan explaining the fundamentals on RAG. I had no idea what RAG even was before watching it. It always seemed like a far fetched concept that would never make sense to someone so young in the industry as me. Nevertheless, I began scouring the web to get a hold of the basics, and unexpectedly, the concept clicked. I recently got back into spamming Blitz games at Chess.com, so I figured building a chess chatbot was the perfect way to learn by doing. RAG stands for Retrieval-Augmented Generation. Instead of just asking an AI a question and hoping it knows the answer, you first retrieve relevant passages from your own documents and then ask the model to answer using those. As a result, you usually receive more grounded and foundationally correct answers from the model. I used three chess PDFs covering openings, middlegames, and endgames as the knowledge base for this, and built the full pipeline from scratch. As usual, Groq came to my rescue as the LLM for the project with the Llama-3.3-70B-Versatile model. ChromaDB was the perfect choice for vector databases for first-timers like me. And well, it turns out chunk size actually matters more than I thought it did. My chunks were too small and kept splitting context mid-thought, so I doubled the size to 1000 characters with 200-character overlap and retrieval got a lot cleaner. Not all embedding models are the same. Swapping from all-MiniLM-L6-v2 to all-mpnet-base-v2 gave me better semantic search and suddenly the chatbot could find stuff it was completely missing before. Querying only n=3 chunks was too stingy. Bumping it to n=10 gave the LLM way more context to work with. With 3 retrieved chunks, it acted as if I asked it about pickleball and not chess. Printing chunk sources and metadata during debugging was a game changer. The model's final answer alone tells you nothing about what actually went wrong in the retrieval step. Doing this sped up my debug game by a mile. It is a naive RAG system by design. I have not implemented any reranking, memory or agents for the sake of simplicity so that I could fully focus on understanding what was happening under the hood. This is pretty much the same reason I repudiated the suggestion of using Streamlit to build a UI and stuck to a CLI (Command Line Interface), which is easier to debug around. Honestly one of the most satisfying 5-hour builds I have done. If you are curious about RAG and do not know where to start, go watch Aishwarya's video. #RAG #LLM #GenerativeAI #MachineLearning #LearningInPublic #Chess #AIProjects #BuildInPublic #NLP #VectorDatabase

Apr 26, 2026

Balaji Kottana

Just wrapped up an incredibly insightful session on **LLM Fine-Tuning** with the amazing **Aishwarya** and **Arvind** on Topmate — and I'm still processing how much ground we covered! 🙌 From LoRA & QLoRA to Supervised Fine-Tuning, RAG optimization, and hands-on tooling with Unsloth and Hugging Face — it was a masterclass in making large language models actually work for real-world use cases. But here's the thing that hit me hardest during the session: Looking at the forward pass diagram — inputs, weights, weighted sums, activation functions — I was reminded that **the fundamentals never go out of style.** In the age of agents and LLMs, it's easy to forget that everything still traces back to classical ML intuition. The math hasn't changed. The thinking hasn't changed. Only the scale has. 🔑 The engineers who truly thrive in the agentic era won't be the ones who only prompt — they'll be the ones who understand *why* the model behaves the way it does. Thank you **Aishwarya Srinivasan** and **Arvind Narayanamurthy** for not just teaching tools, but for reinforcing that deep understanding. Sessions like these are rare. If you haven't explored fine-tuning yet, now is the time. 🚀 #LLMs #FineTuning #MachineLearning #AI #DeepLearning #LoRA #Topmate #ContinuousLearning

Apr 26, 2026

Karthick Nethaji Kaleeswaran

Operations Director, Product Consulting @ HCLTech | Building AI-powered Product Consulting Practice | Agentic AI in enterprise | Strategy Consultant | PMI EVOLVE Speaker

I demo AI features to prospects almost every day. The reactions are genuinely exciting > "this is exactly what we need", "can we go live next month?", "our board has been asking for this". Then they go live. And two weeks later the feedback starts trickling in. Friction here. Unexpected behavior there. "It worked differently in the demo". Nothing broke. But nothing quite held either. I thought this was just my org figuring things out. Then I read this from Aishwarya Srinivasan. "The era of vibe-coding has brought us incredible speed, but it has also introduced a dangerous precedent in production: Vibe-based Evaluation. Vibing your way to production is the fastest path to architectural debt and user churn". That hit uncomfortably close to home. The demo works because we control everything. Production doesn't give us that courtesy. The shift I made: treat demo-readiness and production-readiness as parallel tracks, not sequential phases. The demo gets you in the door. Production discipline is what keeps you there. Shipping fast still matters. But shipping trustworthy is the actual moat. #ProductManagement #AIPM #EnterpriseAI #AIStrategy

Apr 26, 2026

Zeba Karkhanawala

LLMs have always had this aura of mystery around them. Last Saturday, I tried to demystify at least one piece of it. Attended a workshop on fine-tuning LLMs with The Gen Academy, presented by Aishwarya Srinivasan and Arvind Narayanamurthy. I've read about fine-tuning in Chip Huyen's AI Engineering book, but it's such a complex topic that it's genuinely tough to wrap my head around. The workshop helped!! One of the key things I remembered from this workshop was LoRA architecture - instead of retraining a model from scratch, you freeze the original weights and add small trainable layers on top. Same outcome, way less compute. We also did a hands-on supervised fine-tuning lab where we fine-tuned Llama 3.2 on a doctor appointment booking chatbot. The task was intent classification: given a user message, can the model correctly identify what the person wants to do - book, cancel, list appointments. Coming from an ML background, the concept felt familiar but looking at it through an LLM lens was a different exercise - you're not training for fixed, predictable inputs. You're training for anything a user might say. Still have a long way to go with this topic. But Saturday was a good few hours of making it a little less opaque. #AI #LLMs #FineTuning

Apr 26, 2026

Yash Mishra

🚀 Attended an insightful session on Retrieval-Augmented Generation (RAG) Had the opportunity to attend a session focused on RAG, one of the most powerful approaches in modern AI systems. It was great to understand how combining retrieval mechanisms with LLMs can significantly improve accuracy and reduce hallucinations. Looking forward to exploring and implementing RAG in real-world use cases, especially in DevOps and AI-driven systems. ## @Aishwarya Srinivasan

Apr 26, 2026

Sanket Kulkarni

🔍 What happens when your AI confidently gives you the WRONG answer? . . . That's exactly what I uncovered today at the RAG Fundamentals lightning session by Aishwarya and Arvind. And it's not an LLM (ChatGPT, Gemini, Claude) problem. It's a RAG problem because they're trained on internet data, not your organization's internal docs, policies, or real-time databases. So when you ask ChatGPT "Can I share a file with our logistics vendor?" - Without thinking as per the rules, it not just gives you an affirmation but also tells you how to do it, and it violates the actual company policy. Classic hallucination 😵 This is exactly the gap RAG fills. ✅ Here's what I walked away understanding: 💡 What RAG actually does - It connects your LLM to knowledge it was never trained on and makes it cite its sources. You get a "grounded answer". 🧱 How it works (generic architecture) - Documents > Chunks > Embeddings > Vector DB & the importance of Metadata, how numbers capture meaning, chunking strategies, re-ranking, etc. ⚡ Some myths around RAG - like Bigger context windows replace RAG, RAG only works on PDFs, Fine-tuning is better - is it 🤔 🛠️ The hands-on demo - Covering a live RAG chatbot demo built on n8n using just one PDF as the knowledge source. The bot retrieved chunks from Vector DB, passed them through embeddings and answered questions with full source traceability. Watching the pipeline actually execute in real-time made every concept clear 🔥 📏 And yes, RAG isn't magic: • It's not fully real-time (there's a lag) • Access control (RBAC) needs to be built on top - when we go deeper in complex RAG pipelines • Garbage in = garbage out - your docs need to be good That was a jam-packed learning session and I can tell that every minute was worth it, Aishwarya and Arvind made sure of that 💯 More to come as I continue this journey into Agentic AI 🚀 #RAG #GenerativeAI #AgenticAI #LLM #AIEngineering #MachineLearning #TheGenAcademy #AI #VectorDB #LangChain

Apr 26, 2026

Anand Sridharan

Service Delivery Transformation | Leading Intelligent Ops | Data & AI

𝗪𝗵𝘆 𝗔𝗜 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗲𝘀 𝗮𝗻𝗱 𝗛𝗼𝘄 𝗥𝗔𝗚 𝗙𝗼𝗿𝗰𝗲𝘀 𝗧𝗿𝘂𝘁𝗵? Attended 𝗥𝗔𝗚 𝗘𝘅𝗽𝗹𝗮𝗶𝗻𝗲𝗱: 𝗧𝗵𝗲 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 𝗕𝗲𝗵𝗶𝗻𝗱 𝗔𝗴𝗲𝗻𝘁𝗶𝗰 𝗔𝗜 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 lightening session hosted by Aishwarya and Arvind packed with practical use cases, architecture insights, and scalable design patterns for Agentic AI. 𝗧𝗵𝗲 𝗛𝗮𝗿𝗱 𝗧𝗿𝘂𝘁𝗵:,Your LLM will confidently sabotage you unless you anchor it reality. 𝗥𝗲𝗮𝗹-𝗪𝗼𝗿𝗹𝗱 𝗘𝘅𝗮𝗺𝗽𝗹𝗲: Ask "𝘊𝘢𝘯 𝘐 𝘴𝘩𝘢𝘳𝘦 𝘷𝘦𝘯𝘥𝘰𝘳 𝘧𝘪𝘭𝘦𝘴?"→ ChatGPT says "𝘠𝘦𝘴, 𝘩𝘦𝘳𝘦'𝘴 𝘩𝘰𝘸" Your Policy: "𝘕𝘰 𝘦𝘹𝘵𝘦𝘳𝘯𝘢𝘭 𝘴𝘩𝘢𝘳𝘪𝘯𝘨 𝘸𝘪𝘵𝘩𝘰𝘶𝘵 𝘢𝘱𝘱𝘳𝘰𝘷𝘢𝘭" The Gap: Internet-trained LLMs don't know your rules. 𝘎𝘦𝘮𝘪𝘯𝘪-generated session summary image from my notes along with Key Takeaways: 𝗠𝘆𝘁𝗵 𝗕𝘂𝘀𝘁𝗲𝗱: Bigger context windows don't replace retrieval 𝗟𝗶𝘃𝗲 𝗣𝗿𝗼𝗼𝗳: n8n demo turned a PDF into a cited chatbot instantly 𝗥𝗲𝗮𝗹𝗶𝘁𝘆 𝗖𝗵𝗲𝗰𝗸: Lag exists, RBAC needs work, data quality rules all 𝗕𝗼𝘁𝘁𝗼𝗺 𝗟𝗶𝗻𝗲: Stop playing with consumer AI. Build retrieval pipelines that cite your truth or bet your business on fabricated answers. Big thanks to Aishwarya and Arvind for an highly informative session with live demo and Q&A. P.S: Personal views. #EnterpriseAI #RAG #AgenticAI #AIGovernance #AI #SharingInPublic #LearningMindset

Apr 26, 2026

RB

Raghavendra Bagalkoti

Building the Future of Finance with AI + Cloud | VP at Barclays | Simplifying Capital Markets | Thought Leader in Tech-Driven Finance

RAG is Dead!! Content window eliminates RAG!! Yesterday I attended the session “RAG Explained: The Architecture Behind Agentic AI Systems” on Maven, by Aishwarya Srinivasan & Arvind Narayanamurthy, that demystified so many myths about RAG, like the ones above 👆 It was very insightful to learn LLMs alone are not enough. They are powerful reasoning engines, but they do not know your company data, internal documents, or real-time systems. That’s where RAG changes everything. Instead of asking the model to guess, a RAG system: 1️⃣ Retrieves relevant information 2️⃣ Injects it into the prompt 3️⃣ Lets the model reason over actual data In simple terms: LLM = 🧠 Brain RAG = 📚 Memory Agents = 🤖 Workers Put them together and you get modern AI systems that can actually operate in the real world. 👏 Huge thanks to the authors for making a complex topic clear, practical, and accessible. #ArtificialIntelligence #RAG #AgenticAI #AIArchitecture #GenerativeAI #LLM #AIEngineering

Apr 26, 2026

Jeel Jani

AI Product Manager | B2B SaaS | Rapid Prototyping (Cursor, Lovable) & LLM Prompt Engineering | YC Buildspace Alum

This weekend, I attended a RAG workshop by Aishwarya and Arvind - and I'm still thinking about it. Honestly, I went in expecting dense concepts, jargon, and that familiar feeling of nodding along while quietly being lost. What I got instead was clarity. What stood out wasn't just the content - it was how it was delivered. Complex ideas broken down with analogies that just *clicked*. The kind of teaching where you stop and think, "wait, that actually makes so much sense." RAG (Retrieval-Augmented Generation) is one of those topics that sounds intimidating until someone takes the time to meet you where you are. Aishwarya and Arvind did exactly that. Genuinely one of the best learning experiences I've had in a while.Also excited for upcoming workshops by The Gen Academy Thank you Aishwarya Srinivasan and Arvind Narayanamurthy for making something powerful feel accessible. That's a rare skill. #RAG #MachineLearning #AI #LearningAndDevelopment #Workshop

Apr 26, 2026

Arjun Rajan

Senior System Analyst at CSC

Grateful for an incredibly insightful session on Claude Cowork and Claude Code! 🙌 A big thank you to Aishwarya Srinivasan, Arvind Narayanamurthy, and the entire team at The Gen Academy for putting together such a well-structured and engaging session. The depth of knowledge, practical insights, and clarity in explaining real-world applications made the learning experience truly valuable. It’s sessions like these that help bridge the gap between concepts and implementation. Looking forward to applying these learnings and exploring more in this space! #AI #Claude #Learning #TechCommunity #GenAcademy #DeveloperGrowth

Apr 26, 2026

Y

Yogitha R

QA Engineer at Aptean | AI-Native in the Making | LLMs · Agentic AI · GenAI Workflows | Turning 10x Productivity into Reality

This Saturday had a different kind of energy. No plans. Just curiosity - and a session I had been waiting for all week. "The AI PM Playbook: Building AI Products at Scale" by MAVEN. And honestly? It delivered more than I expected. What hit me the most wasn't just the content. It was watching Aishwarya Srinivasan walk through her entire AI stack live - Claude for PRDs and stakeholder narratives, Claude Code for prototyping agents, Lovable for building full apps from prompts without a dev bottleneck. That last one sat with me. No dev bottleneck. As someone who works in a product-based company and has spent time deeply understanding how products are built, tested, and shipped - this felt like a signal I couldn't ignore. The gap between "having product intuition" and "building something with it" is getting smaller every single day. And the people who figure out how to combine product thinking with AI agent workflows? They're going to move at a speed that's just different. I left the session with 3 pages of notes, 2 new tools to try, and 1 very clear thought: The next thing I build won't just use AI. It'll be powered by agents that think, act, and ship alongside me. Massive thank you to Aishwarya Srinivasan, Arvind Narayanamurthy, and Shyamala Prayaga for putting together something real. No fluff. Just depth. If you missed it , keep an eye on MAVEN. Worth every Saturday🙌 #AIProductManagement #BuildingWithAI #AIAgents #ProductThinking #MAVENlearning #ClaudeAI #GenAI #AIWorkflow #WeekendLearning #FutureOfProduct#AIProductManagement #BuildingWithAI #AIAgents #ProductThinking #MAVENlearning #ClaudeAI #GenAI #AIWorkflow #WeekendLearning #FutureOfProduct

Apr 26, 2026

Karthik Malaiyappan

Data | AI | Agentic | FinTech

Thank you so much Aishwarya Srinivasan and Arvind Narayanamurthy for hosting Session on Building AI Products at Scale. Appreciate the effort you put out to brought the knowledge from core of its industry to the people.Looking for more such sessions. Honored to learn some perspective from its core.Shyamala Prayaga you were just amazing throughout the session.The explanation you gave for all the questions is quite understandable in simple words. AI is not thread itself it depends on how we engage it with products we built✨✨ #AI #Agentic #Saturdaylearning #Products

Apr 26, 2026

Mahaveer Satra

Solutions Architect | Digital Twins | Building Agentic AI Systems

I spent 3 months building a multi-agent system that does 2-4 hours of AE work in ~4 minutes. 4 LangGraph agents. Cited sales strategy. Production-grade. 3 things production taught me: -> NAIVE RAG FAILS HARD "GNC toolboxes" returned zero semantic similarity to "Sensor Fusion and Tracking Toolbox." Vector-only search = wrong products in front of customers. Fixed it with Hybrid RAG (thanks Aishwarya Srinivasan): BM25 + vector + cross-encoder reranking. Retrieval quality is the ceiling—everything downstream inherits its failures. -> YOU CAN'T TUNE WITHOUT EVALS Like adjusting PID gains without feedback means you're flying blind. Built Claude-as-judge with 4 metrics + 9 deterministic checks. Went from 3.0 to 4.2 quality score across 3 iterations. -> LLM COST IS A ROUTING PROBLEM Ollama ($0) → Groq 8B → Groq 70B dispatched by task complexity. Prompt caching cuts 90% of token costs. MIT licensed. If any of this saves you a debugging session, fork it, build on it. Repo Link in comments. #AgenticAI #LangGraph #RAG

Apr 26, 2026

Lijo Joseph

Most AI answers 𝘴𝘰𝘶𝘯𝘥 𝘳𝘪𝘨𝘩𝘵… but are they actually correct? One of the challenges with LLMs becomes very clear in enterprise settings. LLMs like ChatGPT, Gemini, or Claude are trained on 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗱𝗮𝘁𝗮, not your company’s internal policies, processes, or documents. So when someone asks: “𝘊𝘢𝘯 𝘐 𝘴𝘩𝘢𝘳𝘦 𝘢 𝘤𝘭𝘪𝘦𝘯𝘵 𝘧𝘪𝘭𝘦 𝘸𝘪𝘵𝘩 𝘢 𝘷𝘦𝘯𝘥𝘰𝘳?” The response may sound confident and logical… but it could still be 𝗺𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗽𝗼𝗹𝗶𝗰𝘆. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻 RAG connects LLMs to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘁𝗵𝗲𝘆 𝘄𝗲𝗿𝗲 𝗻𝗲𝘃𝗲𝗿 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻. Instead of relying only on pre-trained knowledge, it: 1. Breaks documents into 𝗰𝗵𝘂𝗻𝗸𝘀 2. Converts them into 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 (numerical representations of text) 3. Stores them in a 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 4. Uses 𝗰𝗼𝘀𝗶𝗻𝗲 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆(=0.97) to retrieve the most relevant context 5. Feeds that context to the LLM to generate grounded responses 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗵𝗮𝗻 𝗶𝘁 𝗹𝗼𝗼𝗸𝘀 The way you split data directly impacts the quality of retrieval. Common chunking strategies include: • Fixed-length chunking • Sentence-based chunking • Paragraph-based chunking • Sliding window chunking • Semantic chunking • Recursive chunking Better chunking → Better retrieval → Better answers And more importantly: 𝗕𝗲𝘁𝘁𝗲𝗿 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗰𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 Which means the response is not just helpful, but 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗹𝗲: • You can see where the answer came from • You can refer back to the exact company document • The answer has real grounding, not just confidence That’s what gives AI 𝗰𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘂𝘀𝗲. ⚖️𝗥𝗔𝗚 𝘃𝘀 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 → Good for behavior, structure, and low latency 𝗥𝗔𝗚 → Better for dynamic, real-time, and private enterprise data For most enterprise use cases, RAG is the most effective starting point for improving LLM responses. 𝗠𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 AI is not just about generating answers. It’s about: • Retrieving the right context • Grounding responses in real data • Making answers 𝘃𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗰𝗼𝗻𝘃𝗶𝗻𝗰𝗶𝗻𝗴 A quick shoutout to Arvind Narayanamurthy & Aishwarya Srinivasan from The Gen Academy for this weekend learning.

Most AI answers 𝘴𝘰𝘶𝘯𝘥 𝘳𝘪𝘨𝘩𝘵… but are they actually correct? One of the challenges with LLMs becomes very clear in enterprise settings. LLMs like ChatGPT, Gemini, or Claude are trained on 𝗶𝗻𝘁𝗲𝗿𝗻𝗲𝘁 𝗱𝗮𝘁𝗮, not your company’s internal policies, processes, or documents. So when someone asks: “𝘊𝘢𝘯 𝘐 𝘴𝘩𝘢𝘳𝘦 𝘢 𝘤𝘭𝘪𝘦𝘯𝘵 𝘧𝘪𝘭𝘦 𝘸𝘪𝘵𝘩 𝘢 𝘷𝘦𝘯𝘥𝘰𝘳?” The response may sound confident and logical… but it could still be 𝗺𝗶𝘀𝗮𝗹𝗶𝗴𝗻𝗲𝗱 𝘄𝗶𝘁𝗵 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗽𝗼𝗹𝗶𝗰𝘆. 𝗧𝗵𝗶𝘀 𝗶𝘀 𝘄𝗵𝗲𝗿𝗲 𝗥𝗔𝗚 (𝗥𝗲𝘁𝗿𝗶𝗲𝘃𝗮𝗹 𝗔𝘂𝗴𝗺𝗲𝗻𝘁𝗲𝗱 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻) 𝗰𝗼𝗺𝗲𝘀 𝗶𝗻 RAG connects LLMs to 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝗸𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝘁𝗵𝗲𝘆 𝘄𝗲𝗿𝗲 𝗻𝗲𝘃𝗲𝗿 𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗼𝗻. Instead of relying only on pre-trained knowledge, it: 1. Breaks documents into 𝗰𝗵𝘂𝗻𝗸𝘀 2. Converts them into 𝗲𝗺𝗯𝗲𝗱𝗱𝗶𝗻𝗴𝘀 (numerical representations of text) 3. Stores them in a 𝘃𝗲𝗰𝘁𝗼𝗿 𝗱𝗮𝘁𝗮𝗯𝗮𝘀𝗲 4. Uses 𝗰𝗼𝘀𝗶𝗻𝗲 𝘀𝗶𝗺𝗶𝗹𝗮𝗿𝗶𝘁𝘆(=0.97) to retrieve the most relevant context 5. Feeds that context to the LLM to generate grounded responses 𝗖𝗵𝘂𝗻𝗸𝗶𝗻𝗴 𝗶𝘀 𝗺𝗼𝗿𝗲 𝗶𝗺𝗽𝗼𝗿𝘁𝗮𝗻𝘁 𝘁𝗵𝗮𝗻 𝗶𝘁 𝗹𝗼𝗼𝗸𝘀 The way you split data directly impacts the quality of retrieval. Common chunking strategies include: • Fixed-length chunking • Sentence-based chunking • Paragraph-based chunking • Sliding window chunking • Semantic chunking • Recursive chunking Better chunking → Better retrieval → Better answers And more importantly: 𝗕𝗲𝘁𝘁𝗲𝗿 𝗮𝗻𝘀𝘄𝗲𝗿𝘀 𝘄𝗶𝘁𝗵 𝗰𝗶𝘁𝗮𝘁𝗶𝗼𝗻𝘀 Which means the response is not just helpful, but 𝘁𝗿𝗮𝗰𝗲𝗮𝗯𝗹𝗲: • You can see where the answer came from • You can refer back to the exact company document • The answer has real grounding, not just confidence That’s what gives AI 𝗰𝗿𝗲𝗱𝗶𝗯𝗶𝗹𝗶𝘁𝘆 𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲 𝘂𝘀𝗲. ⚖️𝗥𝗔𝗚 𝘃𝘀 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 𝗙𝗶𝗻𝗲-𝘁𝘂𝗻𝗶𝗻𝗴 → Good for behavior, structure, and low latency 𝗥𝗔𝗚 → Better for dynamic, real-time, and private enterprise data For most enterprise use cases, RAG is the most effective starting point for improving LLM responses. 𝗠𝘆 𝘁𝗮𝗸𝗲𝗮𝘄𝗮𝘆 AI is not just about generating answers. It’s about: • Retrieving the right context • Grounding responses in real data • Making answers 𝘃𝗲𝗿𝗶𝗳𝗶𝗮𝗯𝗹𝗲, 𝗻𝗼𝘁 𝗷𝘂𝘀𝘁 𝗰𝗼𝗻𝘃𝗶𝗻𝗰𝗶𝗻𝗴 A quick shoutout to Arvind Narayanamurthy & Aishwarya Srinivasan from The Gen Academy for this weekend learning.

Apr 26, 2026

Michael Galarnyk

Research Scientist Intern @ Adobe | Dell Pro Precision Ambassador | ML Ph.D. @ Georgia Tech

I ran 7 RAG architectures side by side on the same questions to see what actually matters. The setup comes from a deep dive by a friend of mine, Vidhyakshaya Kannan, on The Gen Academy which breaks down these RAG architectures and their tradeoffs. A few things that stood out: - Most architectures land in a similar accuracy range - Agentic RAG performs best, but costs ~4.8× more per correct answer - Self-RAG underperforms Naive RAG. More complexity, worse results It is easy to over-engineer RAG systems. Running them side by side makes that tradeoff very clear. I’ll drop the blog post and code in the comments. How are you deciding between RAG architectures in practice? Optimizing for accuracy, cost, or something else? #MachineLearning #ArtificialIntelligence #DellTech #DellProPrecision #NVIDIA

Apr 26, 2026

Praneeta Pericherla

Incoming MS-DAS student @ Carnegie Mellon University

Most AI PMs are managing probabilistic systems with deterministic tools. That's why AI products quietly rot after launch. In traditional software: a button either works or it doesn't. Bugs are reproducible. If it passes staging, it passes prod. In AI: the "same" input produces different outputs. A prompt that works on 10 examples fails on the 11th. Features don't ship as "works / doesn't work", they ship with a distribution of quality. And most PMs have no idea how to measure that distribution. I mapped out the full AI PM tooling stack for 2026, the 6 layers every AI PM needs to know, the category leaders in each, and the judgment calls that actually matter: → Evaluation (where quality is defined) → Observability (seeing what actually shipped) → Prompt management (governing changes safely) → Prototyping (Bolt, Lovable, v0, Cursor) → Experimentation (shipping to users safely) → Customer feedback (keeping evals honest) The biggest mistake AI PMs make? Adopting an eval tool and never building a real dataset. Treating the tool as the deliverable instead of the scaffolding. Inspired by the Lightning Session on The AI PM Playbook by The Gen Academy, hosted by Aishwarya Srinivasan and Arvind Narayanamurthy, with guest speaker Shyamala Prayaga. Full breakdown on Substack, link in comments 👇

Apr 26, 2026

Rachana NV

Yesterday, I attended the AI PM Playbook by The Gen Academy, featuring Shyamala Prayaga (Senior AI Product Manager at NVIDIA). You might wonder, what’s a software engineer doing in an AI PM session? Because I’ve seen how often we jump straight into building… and only later realize we should’ve asked better questions first. Working closely with PMs, I’ve started valuing that thought process more, how ideas are shaped before they become features. That’s what pulled me into this session. One line from the session that really stayed with me: 👉 Don’t fall for the “shiny object syndrome” in AI. It’s tempting to add AI everywhere, but the real focus should be on: • Solving the right problem (not just using AI for the sake of it) • Defining metrics & evals upfront - “build evals before building features” • Thinking about adoption - token economics, context, and real user value A good reminder that building AI products, at any level, needs clarity, intent, and responsibility, not just hype. #AI #ProductManagement #SoftwareEngineering #GenAI #Learning

Apr 26, 2026

Pawan Simha

Student @ SNPSU | Gemini Certified | Aspiring AI Product Manager | Python • Machine Learning • Web Development | CR & HRD Coordinator

AI products don’t fail because of bad models. They fail because of bad decisions. Over the weekend, I attended the Lightning Lesson on The AI PM Playbook, hosted by Aishwarya Srinivasan and Arvind Narayanamurthy (Maven × The Gen Academy), with Shyamala Prayaga (NVIDIA). It exposed a gap in how I was thinking about AI systems. I was focused on capabilities. This shifted me toward accountability. The MAP Framework (Model → Augment → Program) stood out—not just as a technique, but as a decision filter: 1. When should AI generate? 2. When should it assist? 3. And when should it stay out entirely? This matters because AI is non-deterministic. Treat it like traditional software, and you end up with "silent failures"—systems that appear correct until they break user trust. The shift for me: As someone with a technical background, I used to ask: “What can this model do?” Now, as an aspiring AI PM, I ask: “What should the system be responsible for?” I’m now applying this product-first mindset to my own projects—moving beyond demos toward reliable systems at scale. For AI builders: what’s one decision that becomes harder when moving from demo to production? #ArtificialIntelligence #NVIDIA #AIProductManagement #GenerativeAI #SystemDesign #BuildInPublic #AIEngineering #MachineLearning #ProductStrategy #TheGenAcademy

Apr 26, 2026

Wall of love for The Gen Academy